Rogowski coil current transducer compensation method for harmonic active power error

In the harmonic active power measurement, the highest uncertainties are generally introduced by the current and voltage transducers. In a previous paper, the authors showed that the current transformer (CT) can introduce significant errors in such measurement, especially if the phase shift between voltage and current is close to ±90°. In such condition the errors on harmonic power measurement are mainly due to the CT phase displacement. This paper shows that better results can be achieved with more linear transducers, such as the Rogowski coil current transducers (RCCTs), whose metrological performance in distorted condition can be improved, by means of a proper compensation method. The proposed method for RCCTs compensation is based on the frequency response and it allows to reduce the errors on harmonic power measurement, also for phase shift close to ±90°. The study is supported by several experimental tests.     

基于关键路径延时检测的自适应电压缩减技术

为了减小传统的最差情况设计方法引入的电压裕量,提出了一种变化可知的自适应电压缩减(AVS)技术,通过调整电源电压来降低电路功耗.自适应电压缩减技术基于检测关键路径的延时变化,基于此设计了一款预错误原位延时检测电路,可以检测关键路径延时并输出预错误信号,进而控制单元可根据反馈回的预错误信号的个数调整系统电压.本芯片采用SMIC180 nm工艺设计验证,仿真分析表明,采用自适应电压缩减技术后,4个目标验证电路分别节省功耗12.4％,11.3％,10.4％和11.6％.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Functions of ULK1 in autophagy and non-autophagy pathways and its implications in human physiology and disease

ULK1 (unc-51 like autophagy activating kinase 1), a mammalian serine/threonine kinase, is a key component of autophagy initiation complex and helps to induce all types of autophagy. Canonical autophagy is a process in which, through the interactions of a series of autophagy-related proteins, damaged organelles or misfolded proteins are engulfed by autophagosomes and then merged with lysosomes to be degraded. Thus, canonical autophagy is an important constituent part of the cellular “quality control.” Besides, accumulating evidence indicates that ULK1 exerts autophagy-independent effects in a cell-specific manner. For example, ULK1 facilitates neurite elongation through the regulation of endoplasmic reticulum (ER)–Golgi trafficking in neurons, stimulates phosphopentose pathway to help NADPH (nicotinamide adenine dinucleotide phosphate hydrogen) production, and acts as a duplex regulator in type I IFN (type I interferon) induced innate immune response. Considering the importance and diversity of ULK1 in various biological processes, this review aims to present a comprehensive overview of autophagy and non-autophagy related functions of ULK1 in a variety of human physiological, pathological, and disease processes.     

A self‐scalable load injection service

Load testing of applications is an important and costly activity for software provider companies. Classical solutions are very difficult to set up statically, and their cost is prohibitive in terms of both human and hardware resources. Virtualized cloud computing platforms provide new opportunities for stressing an application's scalability, by providing a large range of flexible and less expensive (pay‐per‐use model) computation units. On the basis of these advantages, load testing solutions could be provided on demand in the cloud. This paper describes a Benchmark‐as‐a‐Service solution that automatically scales the load injection platform and facilitates its setup according to load profiles. Our approach is based on: (i) virtualization of the benchmarking platform to create self‐scaling injectors; (ii) online calibration to characterize the injector's capacity and impact on the benched application; and (iii) a provisioning solution to appropriately scale the load injection platform ahead of time. We also report experiments on a benchmark illustrating the benefits of this system in terms of cost and resource reductions. Copyright © 2013 John Wiley & Sons, Ltd.     

Intelligent Information‐Centric Networking routing scheme under imprecise information

Information‐Centric Networking (ICN) has been accepted to overcome some weaknesses of the current Internet architecture, showing that “what is being exchanged” is more important than “who are exchanging information.” Given the inadequate considerations on Quality of Service (QoS) and energy saving in ICN routing, we propose in this paper a routing algorithm to enhance the two aspects. At first, on one hand, Cauchy distribution is used as a fuzzy model to evaluate users' QoS requirements, such as bandwidth, delay, and error rate; on the other hand, we formulate energy saving problem to evaluate the green quality of routing algorithm. Then, we design a link selection approach by considering QoS and energy saving, which belongs to a multi‐objective decision problem resolved by intelligent drops algorithm. Finally, we implement the proposed algorithm and compare it with the famous adaptive forwarding mechanism in terms of some significant metrics, and the experimental results reveal that the proposed algorithm is more efficient.     

The potential of energy flexibility of space heating and cooling in Portugal

This study assesses the potential of energy flexibility of space heating and cooling for a typical household under different geographical conditions in Portugal. The proposed approach modifies the demand through the optimization of the thermostat settings using a genetic algorithm to reduce either operational costs or interaction with the grid. The results show that the used energy flexibility indicator expresses the available potential and that flexibility depends on several factors, namely: i) thermal inertia of the archetypical household; ii) the time of use electricity tariffs; iii) users’ comfort boundaries; and iv) the geographical location of the houses.     

Demand response for home energy management system

The optimization of energy consumption, with consequent cost reduction, is one of the main challenges for the present and future smart grid. Demand response (DR) program is expected to be vital in home energy management system (HEMS) which aims to schedule the operation of appliances to save energy costs by considering customer convenience as well as characteristics of electric appliances. The DR program is a challenging optimization problem especially when the formulations are non-convex or NP-hard problems. In order to solve this challenging optimization problem efficiently, an effective heuristic approach is proposed to achieve a near optimal solution with low computational costs. Different from previously proposed methods in literatures which are not suitable to be run in embedded devices such as a smart meter. The proposed algorithm can be implemented in an embedded device which has severe limitations on memory size and computational power, and can get an optimal value in real-time. Numerical studies were carried out with the data simulating practical scenarios are provided to demonstrate the effectiveness of the proposed method.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。